--- license: apache-2.0 datasets: - bigcode/the-stack - bigcode/the-stack-v2 - bigcode/starcoderdata - bigcode/commitpack library_name: transformers tags: - code - mlx - mlx-my-repo base_model: JetBrains/Mellum-4b-sft-python model-index: - name: Mellum-4b-sft-python results: - task: type: text-generation dataset: name: RepoBench 1.1 (Python) type: tianyang/repobench_python_v1.1 metrics: - type: exact_match value: 0.2837 name: EM verified: false - type: exact_match value: 0.2987 name: EM ≤ 8k verified: false - type: exact_match value: 0.2924 name: EM verified: false - type: exact_match value: 0.306 name: EM verified: false - type: exact_match value: 0.2977 name: EM verified: false - type: exact_match value: 0.268 name: EM verified: false - type: exact_match value: 0.2543 name: EM verified: false - task: type: text-generation dataset: name: SAFIM type: gonglinyuan/safim metrics: - type: pass@1 value: 0.4212 name: pass@1 verified: false - type: pass@1 value: 0.3316 name: pass@1 verified: false - type: pass@1 value: 0.3611 name: pass@1 verified: false - type: pass@1 value: 0.571 name: pass@1 verified: false - task: type: text-generation dataset: name: HumanEval Infilling (Single-Line) type: loubnabnl/humaneval_infilling metrics: - type: pass@1 value: 0.8045 name: pass@1 verified: false - type: pass@1 value: 0.4819 name: pass@1 verified: false - type: pass@1 value: 0.3768 name: pass@1 verified: false --- # cnfusion/Mellum-4b-sft-python-mlx-8Bit The Model [cnfusion/Mellum-4b-sft-python-mlx-8Bit](https://huggingface.co/cnfusion/Mellum-4b-sft-python-mlx-8Bit) was converted to MLX format from [JetBrains/Mellum-4b-sft-python](https://huggingface.co/JetBrains/Mellum-4b-sft-python) using mlx-lm version **0.22.3**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("cnfusion/Mellum-4b-sft-python-mlx-8Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```